Methods and systems for processing crowd-sensed data

ABSTRACT

The disclosed embodiments illustrate methods and systems for processing crowd-sensed data. The method includes receiving the crowd-sensed data from a mobile device associated with a user. The crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure. Thereafter, the event may be prioritized based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event. Further, a notification of the event may be transmitted to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.

TECHNICAL FIELD

The presently disclosed embodiments are related, in general, to acrowdsourcing system. More particularly, the presently disclosedembodiments are related to methods and systems for processingcrowd-sensed data.

BACKGROUND

Development in a city may involve development of infrastructure. In anembodiment, the development of said infrastructure may includedeveloping public infrastructure such as, but not limited to, roads,hospitals, and metros. The development of infrastructure may alsoinclude the development of private infrastructure such as, but notlimited to, housing societies, private homes, and the like.

The infrastructure may require regular maintenance to provide seamlessservice to the public. To determine the maintenance need in the saidinfrastructure, one or more sensors may be installed to monitor thecondition of the infrastructure. For instance, sensors/cameras may beinstalled to determine or detect potholes on the road. However,processing the data captured through the sensors may have variouslimitations that are inherent to the techniques used for detecting suchaberrations in the infrastructure (e.g., image processing and machinelearning techniques). For instance, image processing techniques may belimited by the lighting conditions in which the image was captured bythe sensor. Further, the machine learning techniques may be constrainedby the robustness of data based on which the system was trained.

SUMMARY

According to embodiments illustrated herein there is provided a methodfor processing crowd-sensed data. The method includes receiving, by oneor more processors, the crowd-sensed data from a mobile deviceassociated with a user. The crowd-sensed data corresponds to metadata ofan event pertaining to an aberration in at least one of a publicservice, a public infrastructure, a private service, or a privateinfrastructure. Thereafter, the event may be prioritized by the one ormore processors based at least on a type of the event, a measure ofimpact of the event, or a measure of urgency to resolve the event.Further, a notification of the event may be transmitted by the one ormore processors to an organization responsible to at least resolve theevent, based on the prioritizing, wherein the notification comprises atleast the metadata.

According to embodiments illustrated herein there is provided a systemfor processing crowd-sensed data. The system includes one or moreprocessors configured to receive the crowd-sensed data from a mobiledevice associated with a user. The crowd-sensed data corresponds tometadata of an event pertaining to an aberration in at least one of apublic service, a public infrastructure, a private service, or a privateinfrastructure. The one or more processors are further configured toprioritize the event based at least on a type of the event, a measure ofimpact of the event, or a measure of urgency to resolve the event.Furthermore, the one or more processors are configured to transmit anotification of the event to an organization responsible to at leastresolve the event, based on the prioritizing, wherein the notificationcomprises at least the metadata.

According to embodiment illustrated herein there is provided a computerprogram product for use with a computer. The computer program productcomprising a non-transitory computer readable medium. The non-transitorycomputer readable medium stores a computer program code for processingcrowd-sensed data. The computer program code is executable by one ormore processors to receive the crowd-sensed data from a mobile deviceassociated with a user, wherein the crowd-sensed data corresponds tometadata of an event pertaining to an aberration in at least one of apublic service, a public infrastructure, a private service, or a privateinfrastructure. The event is prioritized based at least on a type of theevent, a measure of impact of the event, or a measure of urgency toresolve the event. A notification of the event is transmitted to anorganization responsible to at least resolve the event, based on theprioritizing, wherein the notification comprises at least the metadata.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings illustrate various embodiments of systems,methods, and other aspects of the disclosure. Any person having ordinaryskill in the art will appreciate that the illustrated element boundaries(e.g., boxes, groups of boxes, or other shapes) in the figures representone example of the boundaries. It may be that in some examples, oneelement may be designed as multiple elements or that multiple elementsmay be designed as one element. In some examples, an element shown as aninternal component of one element may be implemented as an externalcomponent in another, and vice versa. Furthermore, elements may not bedrawn to scale.

Various embodiments will hereinafter be described in accordance with theappended drawings, which are provided to illustrate, and not limit, thescope in any manner, wherein similar designations denote similarelements, and in which:

FIG. 1 is a system environment diagram, in which various embodiments maybe implemented;

FIG. 2 is a message flow diagram illustrating sharing of themessages/data among the entities of the system environment, inaccordance with at least one embodiment;

FIG. 3 is a block diagram of a user computing device, in accordance withat least one embodiment;

FIG. 4 is a flowchart illustrating a method for capturing an event, inaccordance with at least one embodiment;

FIG. 5 is a block diagram of an application server, in accordance withat least one embodiment; and

FIG. 6 is a flowchart illustrating a method for processing crowd-senseddata, in accordance with at least one embodiment.

DETAILED DESCRIPTION

The present disclosure is best understood with reference to the detailedfigures and descriptions set forth herein. Various embodiments arediscussed below with reference to the figures. However, those skilled inthe art will readily appreciate that the detailed descriptions givenherein with respect to the figures are simply for explanatory purposes,as the methods and systems may extend beyond the described embodiments.For example, the teachings presented and the needs of a particularapplication may yield multiple alternate and suitable approaches toimplement the functionality of any detail described herein. Therefore,any approach may extend beyond the particular implementation choices inthe following embodiments described and shown.

References to “one embodiment,” “at least one embodiment,” “anembodiment,” “one example”, “an example”, “for example” and so on,indicate that the embodiment(s) or example(s) so described may include aparticular feature, structure, characteristic, property, element, orlimitation, but that not every embodiment or example necessarilyincludes that particular feature, structure, characteristic, property,element, or limitation. Furthermore, repeated use of the phrase “in anembodiment” does not necessarily refer to the same embodiment.

“Crowd-sensed data” may refer to any data that has been collected fromone or more users around the globe. For example, the one or more usersmay be from a city and may have observed an aberration in at least oneof a public service, a public infrastructure, a private service, or aprivate infrastructure. Further, the one or more users may also report anatural disaster or an environmental problem/issue. The one or moreusers may have recorded the aberration/issue and accordingly reportedthe incident. Such reporting of the incident by the one or more usersmay correspond to the crowd-sensed data. The term “crowd-sensed data” isinterchangeably referred as “event report” hereinafter.

A “user” refers to an individual who registers with for reporting ofevents through his/her computing devices. In an embodiment, the user mayprovide the crowd-sensed data associated with an event, when the usernotices any event in his/her vicinity. The term “user” has beeninterchangeably referred as “resident”, “citizen”, “individual”, or“user” hereinafter.

An “event” refers to an aberration in at least one of a public service,a public infrastructure, a private service, or a private infrastructure.In an embodiment, the event may also correspond to a natural disaster oran environmental problem/issue. In an embodiment, the event may reportedby at least one user.

“Public infrastructure/service” refers to an infrastructure/service thatmay be provided by a government of a nation for public use. Someexamples of public infrastructure/service may include, but are notlimited to, roads, government buildings, street lights, publictransportation system, water supply systems, etc.

“Private infrastructure/service” refers to a privately ownedinfrastructure/service. In an embodiment, the privately ownedinfrastructure/service may include, but are not limited to, a housingsociety, a private home or an apartment, etc.

A “type of event” refers a category allocated to an event. In anembodiment, the one or more categories of an event may be predefined.For example, the one or more categories of event may include, but arenot limited to, traffic congestion, potholes on road, street lamp notoperational, fire in a building, etc. Further, in an embodiment, acategory may be dynamically added to the list of one or more categories,in case an event does not fall under any category from the one or morecategories.

An “impact of an event” refers to an effect of the event on the public.For example, water logging on the roads may lead to traffic congestion,which may further impact the public on the road.

A “measure of urgency” may refer to at least a turn-around time toresolve the event. For example, a fire event reported may be acted uponimmediately as fire may destroy infrastructure on a large scale.Further, the fire event may pose threat to human life. Therefore, thefire event may be assigned a high measure of urgency in comparison toany other event such as water leakage or traffic congestion.

A “notification” refers to a message signal transmitted by the one ormore users to report an event. In an embodiment, the notification mayinclude at least the metadata associated with an event. In anembodiment, the metadata associated with an event may include, but isnot limited to, at least one of content, a location of the user, a timeof sending the metadata, or an event tag, associated with the event. Inan embodiment, the content may include an image captured using an imagecapturing device, a video of an event, a voice recording, etc.

A “quality metric” corresponds to a measure of quality of the content inthe metadata. For instance, if the content corresponds to a voicerecording, the quality metric may be determined based at least on abackground noise in the voice recording. In another example, if thecontent corresponds to an image, the quality metric may be determinedbased at least on the resolution of the image, noise in the image, etc.

“Predetermined threshold” refer to a lower limit of the quality metricbelow which the content is not acceptable.

An “event tag” corresponds to a tag or a category assigned by a user,reporting an event, to the event. For example, the user may be reportinga water logging event. In such a scenario, the user may assign a tag tothe event as “water logging”.

A “historical data” corresponds to a repository of events that have beenreported by one or more users in the past. In an embodiment, thehistorical data may include information pertaining to a time at whichthe event was reported, the type of the event, the impact of the event,and the urgency of the event.

“Remuneration” refers to rewards received by the one or more users forreporting an event. In an embodiment, the remuneration is a monetarycompensation received by the one or more users. However, a person withordinary skill in the art would appreciate that the scope of thedisclosure is not limited to remunerating the one or more users withmonetary compensation. In an embodiment, various other means ofremunerating the one or more users may be employed such as, but notlimited to, remunerating the one or more users with lottery tickets,giving gift items, shopping vouchers, and discount coupons. In anotherembodiment, the remuneration may further correspond to strengthening ofthe relationship between the one or more users and the event reportingsystem. For example, the reporting system may provide the users areputation score based on the events reported by the users. A personskilled in the art would understand that a combination of any of theabove-mentioned means of remuneration could be used for remunerating theone or more users.

“Crowdsourcing” refers to collecting information by soliciting theparticipation of loosely defined groups of individual users. Forexample, the information may correspond reporting an event by a group ofusers.

“One or more policies” refer to a plan of action proposed or otherwiseaccepted to be a set of rules governing an aspect of functioning of anorganization. In an embodiment, the organization may implement the oneor more policies to perform its functions. For example, an organizationsuch as a traffic authority may set one or more policies to governtraffic movement on roads, e.g., one ways routes, speed limits, trafficlights, and so on.

FIG. 1 is a system environment 100, in which various embodiments may beimplemented. The system environment 100 includes one or more usercomputing devices 102 a, 102 b, and 102 c (hereinafter referred as usercomputing devices 102), a network 104, an application server 106, and adatabase server 108.

The user computing devices 102 correspond to computing devices that areusable by the one or more users. In an embodiment, the user computingdevices 102 may be used for reporting an event. In an embodiment, theevent corresponds to an aberration in at least one of a public service,a public infrastructure, a private service, or a private infrastructure.Further, in an embodiment, the event may also correspond to a naturaldisaster or an environmental problem/issue. Examples of the eventinclude traffic congestion, fire accidents, water leakage, etc. (denotedby 110 in FIG. 1). To report the event, the user computing devices 102may include one or more sensors such as, but not limited to, a camera,audio recorder, video recorder, etc., to capture the event. Such datacaptured by the user computing devices 102 to report the event ishereinafter referred as crowd-sensed data. The user computing devices102 may create the crowd-sensed data using the information capturedthrough the one or more sensors. Thereafter, the crowd-sensed data istransmitted to the application server 106. In an embodiment, a user of arespective user computing device (e.g., 102 a) may provide an inputindicative of a tag assigned to the event by the user. The user mayfurther provide an input to describe the event. In an embodiment, theuser computing devices 102 may provide a user interface to the user toprovide such inputs. In an embodiment, the user computing device (e.g.,102 a) may correspond to a mobile device. A person having ordinary skillin the art would understand that the scope of the disclosure is notlimited to the user computing devices 102 as the mobile devices. In anembodiment, the user computing devices 102 may be any computing devicethat has the capability to capture an event and receive the user input.Some examples of the user computing devices 102 may include, but are notlimited to, a desktop, a laptop, a personal digital assistant (PDA), atablet computer, and the like.

The network 104 corresponds to a medium through which content andmessages flow between various devices of the system environment 100(e.g., the user computing devices 102, the application server 106, andthe database server 108). Examples of the network 104 may include, butare not limited to, a Wireless Fidelity (Wi-Fi) network, a Wireless AreaNetwork (WAN), a Local Area Network (LAN), or a Metropolitan AreaNetwork (MAN). Various devices in the system environment 100 can connectto the network 104 in accordance with various wired and wirelesscommunication protocols such as Transmission Control Protocol andInternet Protocol (TCP/IP), User Datagram Protocol (UDP), and 2G, 3G, or4G communication protocols.

In an embodiment, the application server 106 may receive thecrowd-sensed data, pertaining to one or more events, from the usercomputing devices 102. As discussed above, the crowd-sensed dataincludes metadata corresponding to the event. In an embodiment, themetadata associated with the event may include at least one of content,a location of the user computing device (e.g., 102 a), a time of sendingthe metadata, or an event tag, associated with the event. In anembodiment, the application server 106 may determine a type of each ofthe one or more events based on respective metadata associated with eachof the one or more events. Thereafter, the application server 106 mayprioritize the one or more events. Based on the prioritization, theapplication server 106 may generate a notification. Further, theapplication server 106 may transmit the notification to a concernedauthority as a task. In an embodiment, the task may have a service levelagreement (SLA) associated with it. Further, the application server 106may store the notification in the database server 108 as historicaldata. In an embodiment, the application server 106 may extract thehistorical data pertaining to the one or more events from the databaseserver 108 to create a statistical model. The statistical model may beutilized to predict an occurrence of the one or more events in thefuture. In an embodiment, the application server 106 may be realizedthrough various types of servers such as, but not limited to, Javaserver, .NET framework, and Base4 server. The operation of theapplication server 106 has been described later in conjunction with FIG.3.

In an embodiment, the database server 108 is operable to store thehistorical data. In an embodiment, the historical data may include oneor more previous occurrences of the one or more events. Further, thedatabase server 108 may store information pertaining to the one or moreusers and their respective user profiles. In an embodiment, the databaseserver 108 may receive a query from the application server 106 toextract at least the historical data or the user profiles of the one ormore users. The database server 108 may be realized through varioustechnologies such as, but not limited to, Microsoft® SQL server, Oracle,and My SQL. In an embodiment, the application server 106 may connect tothe database server 108 using one or more protocols such as, but notlimited to, Open Database Connectivity (ODBC) protocol and Java DatabaseConnectivity (JDBC) protocol.

The operations of the system environment 100 are described inconjunction with FIG. 2.

FIG. 2 is a message flow diagram 200 illustrating sharing of themessages/data among the entities of the system environment 100, inaccordance with at least one embodiment. The message flow diagram 200 isdescribed in conjunction with FIG. 1.

The user computing device 102 a may receive an input from the user toinstall an application (depicted by 202). In an embodiment, theapplication enables the capturing and reporting of the event. In anembodiment, the user computing device 102 a may receive the applicationfrom an application store/marketplace such as Google Play®, WindowsStore®, or Apple iTunes Store®. Post installation of the application,the user may register with the application server 106 (depicted by 204).In an embodiment, registering with the application server 106 mayinvolve creating a user account.

On receiving the request to create the user account, the applicationserver 106, creates a user profile of the user (depicted by 206). Theapplication server 106 stores the user profile of the user in thedatabase server 108 (depicted by 208). Further, the application server106 may send a notification to the user, indicative of the successfulregistration (depicted by 210).

The user of the user computing device 102 a may come across anaberration in the public infrastructure/service which he/she may want toreport. In such a scenario, the user of the user computing device 102 amay capture the event using the one or more sensors in the mobilecomputing device 102 a (depicted by 212). In an embodiment, the user mayutilize the application to capture the event. In an embodiment,capturing the event may further involve assigning the event tag and theevent description to the captured aberration. The application in theuser computing device 102 a may create the crowd-sensed data. In anembodiment, the crowd-sensed data may include the event and associatedmetadata thereof. The user computing device 102 a may send thecrowd-sensed data to the application server 106 (depicted by 214).

On receiving the crowd-sensed data, the application server 106 mayvalidate the crowd-sensed data (depicted by 216). The validation of thecrowd-sensed data is described later in conjunction with FIG. 4. Postthe validation of the crowd-sensed data, the application server 106 maysend a notification to appropriate authority as a task (depicted by218). In an embodiment, the task may have an associated SLA according towhich the authority may resolve the event. Concurrently, the applicationserver 106 may update the historical data in the database server 220.Further, the application server 106 may remunerate the user of the usercomputing device 102 a for reporting the event (depicted by 220).

FIG. 3 is a block diagram of the user computing device 102 a, inaccordance with at least one embodiment. The user computing device 102 aincludes a first processor 302, a first memory 304, a first transceiver306, and an image capturing device 308. The block diagram of the usercomputing device (e.g., 102 a) has been described in conjunction withFIG. 1 and FIG. 2.

The first processor 302 is coupled to the first memory 304, the firsttransceiver 306, and the image capturing device 308. The first processor302 includes suitable logic, circuitry, and/or interfaces that areoperable to execute one or more instructions stored in the first memory304 to perform predetermined operations. The first memory 304 may beoperable to store the one or more instructions. The first processor 302may be implemented using one or more processor technologies known in theart. Examples of the first processor 302 may include, but are notlimited to, an X86 processor, a RISC processor, an ASIC processor, aCISC processor, or any other processor.

The first memory 304 stores a set of instructions and data. Some of thecommonly known memory implementations include, but are not limited to, arandom access memory (RAM), a read only memory (ROM), a hard disk drive(HDD), and a secure digital (SD) card. Further, the first memory 304includes the one or more instructions that are executable by the firstprocessor 302 to perform specific operations. It will be apparent to aperson having ordinary skills in the art that the one or moreinstructions stored in the first memory 304 may enable the hardware ofthe user computing device 102 a to perform the predetermined operations.

The first transceiver 306 transmits and receives messages and datato/from various devices of the system environment 100 (e.g., theapplication server 106, and the database server 108). Examples of thefirst transceiver 306 may include, but are not limited to, an antenna,an Ethernet port, a USB port or any other port that can be configured toreceive and transmit data. The first transceiver 306 transmits andreceives data/messages in accordance with the various communicationprotocols such as, but not limited to, TCP/IP, UDP, and 2G, 3G, or 4Gcommunication protocols. In another embodiment, the first transceiver306 may be configured to receive GPS coordinates from one or more GPSsatellites. The GPS coordinates are utilizable to determine the locationof the user computing device 102 a. In yet another embodiment, thelocation of the user computing device 102 a may be determined by atriangulation method.

The image capturing device 308 is configured to capture at least animage or a video of the event. In an embodiment, the image capturingdevice 308 may include a charge coupled device (CCD) based sensor or aCMOS sensor that may be used for capturing the event. The imagecapturing device 308 may store the captured image in the first memory304. Further, the first processor 302, prior to transmitting the imageto the application server 106, may process the stored image. In anembodiment, the image capturing device 308 may correspond to an embeddedcamera within the user computing device (e.g., 102 a).

The operation of the user computing device 102 a has been described inconjunction with FIG. 4.

FIG. 4 is a flowchart 400 illustrating a method of capturing the event,in accordance with at least one embodiment. The flowchart 400 has beendescribed in conjunction with FIG. 1, FIG. 2, and FIG. 3.

At step 402, one or more user credentials are transmitted to theapplication server 106. In an embodiment, the first processor 302transmits the one or more user credentials. Prior to transmitting theone or more user credentials, the first processor 302 may receive anapplication that is utilizable to capture the one or more events. Theuser of the user computing device 102 a may install the application.Post installation of the application, the user may register with theapplication server 106 through the installed application. In anembodiment, the registration of the user may involve creating a userprofile by the user. In an embodiment, the user profile may containinformation pertaining to a name of the user, a password, a job profileof the user, hobbies of the user, etc.

After the registration, the user may login to the application server 106by transmitting the one or more user credentials to the applicationserver 106. The application server 106 may authenticate the one or moreuser credentials and accordingly send a notification to the usercomputing device 102 a. In an embodiment, the notification may beindicative of at least a successful authentication of the user.

At step 404, an event is captured. In an embodiment, the first processor302 captures the event. In an embodiment, the user of the user computingdevice 102 a may come across an aberration related to the publicinfrastructure/service or private infrastructure/service. Further, theuser may wish to report a natural disaster or an environmentalproblem/issue that the user notices in his/her surroundings. In such ascenario, the user may utilize the installed application (post theauthentication) to capture the event. In an embodiment, the installedapplication provides a user interface to the user, which allows the userto access the image capturing device 308 of the user computing device102 a to capture the event. In an embodiment, the user may capture animage of the event. In another embodiment, the user may capture a videoof the event.

Post capturing of the video/image of the event, the first processor 302may present a user interface to the user. In an embodiment, the userinterface allows the user to input a location of the user, assign a tagto the event, input a description of the event, a time at which theevent was captured, etc. In an embodiment, the location of the user, theevent tag, the description of the event, the time at which the event wascaptured, etc., may constitute the metadata associated with the event.In an embodiment, the event tag may correspond to a type of event. In anembodiment, the user interface of the application may provide a dropdown menu to the user, which has one or more predefined event tags fromwhich the user may select an appropriate tag. Further, in an embodiment,the location of event may be determined based on the GPS coordinatescaptured by the first transceiver 306. In alternate embodiment, the usermay manually input the location of the event.

At step 406, the crowd-sensed data is generated. In an embodiment, thefirst processor 302 generates the crowd-sensed data. In an embodiment,the crowd-sensed data includes the metadata of the event.

At step 406, the crowd-sensed data is transmitted to the applicationserver 106. In an embodiment, the first processor 302 transmits thecrowd-sensed data to the application server 106 through the firsttransceiver 306.

At step 408, remuneration is received by the user. In an embodiment, theprocessor 202 may receive a notification from the application server 106indicative of the remuneration.

FIG. 5 is a block diagram of the application server 106, in accordancewith at least one embodiment. In an embodiment, the application server106 includes a second processor 502, a second memory 504, and a secondtransceiver 506.

The second processor 502 is coupled to the second memory 504, and thesecond transceiver 506. The second processor 502 includes suitablelogic, circuitry, and/or interfaces that are operable to execute one ormore instructions stored in the second memory 504 to performpredetermined operations. The second memory 504 may be operable to storethe one or more instructions. The second processor 502 may beimplemented using one or more processor technologies known in the art.Examples of the second processor 502 may include, but are not limitedto, an X86 processor, a RISC processor, an ASIC processor, a CISCprocessor, or any other processor.

The second memory 504 stores a set of instructions and data. Some of thecommonly known memory implementations may include, but are not limitedto, a random access memory (RAM), a read only memory (ROM), a hard diskdrive (HDD), and a secure digital (SD) card. Further, the second memory504 includes the one or more instructions that are executable by thesecond processor 502 to perform specific operations. It will be apparentto a person having ordinary skills in the art that the one or moreinstructions stored in the second memory 504 may enable the hardware ofthe application server 106 to perform the predetermined operations.

The second transceiver 506 transmits and receives messages and datato/from various devices of the system environment 100 (e.g., the usercomputing devices 102, and the database server 108). Examples of thesecond transceiver 506 may include, but are not limited to, an antenna,an Ethernet port, a USB port or any other port that can be configured toreceive and transmit data. The second transceiver 506 transmits andreceives data/messages in accordance with the various communicationprotocols such as, TCP/IP, UDP, and 2G, 3G, or 4G communicationprotocols. The operation of the application server 106 has beendescribed later in conjunction with FIG. 6.

FIG. 6 is a flowchart 600 illustrating a method for processing thecrowd-sensed data, in accordance with at least one embodiment. Theflowchart 600 has been described in conjunction with FIG. 1, FIG. 2,FIG. 3, FIG. 4, and FIG. 5.

At step 602, the crowd-sensed data is received from the user computingdevices 102. In an embodiment, the second processor 502 receives thecrowd-sensed data through the second transceiver 506. Prior to receivingthe crowd-sensed data, the second processor 502 receives the one or moreuser credentials from the user computing devices 102. In an embodiment,the second processor 502 may authenticate the user by comparing thereceived one or more user credentials with the one or more credentialsstored in the database server 108. Post authenticating the one or moreuser-credentials, the second processor 502 may receive the crowd-senseddata from the user computing devices 102. In an embodiment, thecrowd-sensed data received from the user computing devices 102 includesthe metadata pertaining to the events captured by the respective usercomputing devices 102. The following table illustrates an example of themetadata pertaining to the one or more events received from the usercomputing devices 102:

TABLE 1 Example of metadata pertaining to the one or more events EventsEvent tag Location Time Event-1 Fire 28.6100° N, 01:43PM 77.2300° EEvent-2 Traffic 28.7100° N,  5:43PM congestion 77.3300° E Event-3Potholes 28.7100° N, 10:00AM 77.4300° E Event-4 Water 28.2100° N,12:00PM leakage 77.9300° EReferring to Table 1, the metadata for the event-1 includes the eventtag “fire”. Further, location of the event-1 is “28.6100° N, 77.2300° E”and the time of the reporting of the event is 1:43 PM. Some examples ofknown event types have been described later.

At step 604, the metadata associated with the crowd-sensed data isvalidated. In an embodiment, the second processor 502 is configured tovalidate the metadata. In an embodiment, the validation of the metadataincludes at least one of ensuring data quality, data completeness, anddata consistency.

Data Quality

In an embodiment, the second processor 502 extracts the content capturedby the user from the metadata associated with the event. As describedabove, the content may be a video file or an image file associated withthe event. Thereafter, the second processor 502 may utilize one or moremachine learning techniques to analyze the content. In an embodiment,the second processor 502 determines whether the content is noisy. Someexamples of the one or more machine learning techniques may include, butare not limited to, support vector machine (SVM), neural networks,genetic algorithm, etc. In an embodiment, the second processor 502 mayassign a score to the content based on the analysis. In an alternateembodiment, the second processor 502 may categorize the content in oneor more predefined categories based on the quality of the content.Further, the categories may be indicative of the score of the content.

For example, the crowd-sensed data received for an event by the secondprocessor 502 includes a first content and a second content. The firstcontent is a blurry image while the second content is a clean image. Thesecond processor 502 may assign a higher score to the second content ascompared to the first content. In another embodiment, the secondprocessor 502 may categorize the first content under the noisy imagecategory while the second content may be categorized under the cleanimage category.

Post determining the score of the content, the second processor 502 maycompare the score with a predetermined threshold. In an embodiment, thepredetermined threshold may correspond to an upper limit of the scorebelow which the content is considered as noisy. If the content is noisy,the second processor 502 may discard the content. Further, if the secondprocessor 502 determines that the content is not noisy, the content isselected for further processing.

A person having ordinary skill in the art would understand that thescope of the disclosure is not limited to determining the quality of thecontent by using the one or more machine learning techniques. Variousother techniques may be used for determining the quality of the contentsuch as image processing techniques.

Data Completeness

In addition to the quality of the content, the second processor 502 mayalso determine the completeness of the content. In an embodiment, thecompleteness of the content may correspond to a determination of whetheror not the information present in the metadata is complete. For example,the second processor 502 may receive crowd-sensed data reporting anevent of traffic congestion. However, if the metadata does not includeinformation pertaining to the location of the traffic congestion, thesecond processor 502 may mark such content as incomplete.

Considering another example in which the second processor 502 receivescrowd-sensed data reporting a fire event. However, if the crowd-senseddata does not include information pertaining to the time of the event,the second processor 502 may mark such event as incomplete.

Data Consistency

In an embodiment, the second processor 502 may validate the metadata forpresence of garbage values. To check the metadata for garbage values,the second processor 502 may first determine a data type of theinformation. For example, latitude and longitude may have the data type“numeral”. Further, time field in the metadata may have the data type of“date-time”. Thereafter, the second processor 502 determines thatwhether or not the information present in the metadata is in accordancewith the respective data types. For example, if the coordinates orlocation field in the metadata includes alphabets, the data in thelocation field may not be consistent, and hence may contain garbagevalues.

A person having ordinary skill in the art would understand that thescope of the disclosure is not limited to checking the data consistencybased on the data types. In an embodiment, the processor 502 may employone or more rules to determine data consistency in the metadata. Forexample, the processor may employ a rule that if the time field withinthe metadata includes numeral greater than “24” then the data isinconsistent. Similar rules may be employed on other fields in themetadata.

In an embodiment, the validation further includes determining a count ofreports of the event received from a particular area/location. Forexample, if a fire incident occurs in a populated place, many people mayobserve this fire incident and some of these people may this event tothe application server 106 using the application installed on therespective user computing devices 102. Since the application server 106receives many reports of the fire incident from the same location, theapplication server 106 may validate this report to be legitimate. Forinstance, if the fire incident is reported by a single user, theapplication server 106 may mark the incident as non-legitimate owing tothe fact that an incident like a fire incident occurring in a populatedarea generally catches the attention of a large crowd. Hence, at least afew people (say 10 or more) should have reported the incident, makingthe report received from the single user seem suspicious/non-legitimate.

At step 606, a type of the event is determined. In an embodiment, thesecond processor 502 determines the type of the event. As discussed, thesecond processor 502 may receive the crowd-sensed data from the usercomputing devices 102. The second processor 502 may extract the metadatafrom the crowd-sensed data received from each user-computing device(e.g., 102 a). Based on the event tag assigned by the user (asdetermined from the event metadata), the second processor 502 maydetermine the type of the event.

In a scenario, where the event tag is not present in the metadata, thesecond processor 502 may analyze the content in the metadata using oneor more image processing techniques. The second processor 502 mayextract a set of sample images from the database server 108. Thereafter,the second processor 502 may determine a similarity between the contentimage and the set of sample images by utilizing Scale-Invariant FeatureTransform (SIFT) algorithm. In an embodiment, the set of sample imagesmay be pre-tagged. In an embodiment, the pre-tagged images maycorrespond to the event type to which the sample images correspond.Based on the similarity between the content image and the set of sampleimages, the second processor 502 may determine the type of the event.

For example, the metadata received by the second processor 502 does notinclude the event tag. In such a scenario, the second processor 502extracts a set of sample images from the database server 108. Eachsample image may correspond to a particular event type. For instance,the set of sample images may include an image of a fire event, an imageof a pothole, and an image of a water leakage, and so on. The secondprocessor 502 compares the content image with each image in the set ofsample images. If the content image is determined to be similar to thepothole image, the metadata of the event is considered to be of the type“potholes”.

A person having ordinary skill in the art would understand that thescope of the disclosure is not limited to determining the type of theevent based on the comparison of the content image with the set ofsample images. In an embodiment, the second processor 502 may beutilized to train a classifier based on the set of sample images. In anembodiment, the second processor 502 may utilize a Hough transform or aGabor filter to train the classifier. In an embodiment, the training ofthe classifier may involve a creation of the one or more categoriescorresponding to the event type. The content image is analyzed by thesecond processor 502 to determine image features. Thereafter, based onthe image features, the second processor 502 may categorize the contentimage in at least one category from the one or more categories.

In a scenario, where the second processor 502 is not able to categorizethe content image in any category, the second processor 502 may transmitthe content image to a crowdsourcing platform such as Amazon MechanicalTurk™, CrowdFlower™ as a task. The crowdsourcing platform may thentransmit the content image to one or more crowdworkers, who then assignan event tag to the image. Thereafter, the second processor 502 mayreceive this event tag from the crowdsourcing platform through thesecond transceiver 506. The second processor 502 updates the classifierbased on the event tag received from the crowdsourcing platform.Further, the second processor 502 may store the event tag in thedatabase server 108. In an embodiment, the second processor 502 maycreate a new category for the event based on the event tag of the event,if the content image does not fit into any of the existing categories.

In an embodiment, the classifier may used for validating the event tagsin the metadata. In an embodiment, the second processor 502 may utilizethe classifier to analyze the content image and accordingly classify thecontent image in one of the one or more categories. Thereafter, thesecond processor 502 may extract the event tag from the metadata. Thesecond processor 502 may compare the event tag with the category of theevent. If the category and the event tag matches, the second processor502 validates the event to be correct.

At step 608, the events are prioritized. In an embodiment, the secondprocessor 502 may prioritize the events received in the form of thecrowd-sensed data. In an embodiment, the second processor 502 maydetermine the priority of the event based on one or more attributes suchas, but not limited to, an impact of the event, an urgency to resolvethe event, a probability of event occurrence, and so on. In anembodiment, the information pertaining to the one or more attributes isdetermined from the civic bodies of the city. For instance, in a citylike Venice, a high tide may cause flooding in the city. This floodingmay impact the day-to-day work in the city. Further the occurrence offlooding is very often in Venice and is dependent on high tide. As theimpact is high, the urgency to resolve such an issue may be high.Therefore, the priority of flooding event may be high in comparison toother events in the city of Venice. For instance, if there is a potholeon a road in Venice, and concurrently there is flooding due to hightide, the flooding event is assigned a higher priority.

The statistics of the events with respect to the one or more attributesis obtained from the civic bodies of the city. The following tableillustrates an example of statistics obtained from the civic bodies ofthe city:

TABLE 2 Example of statistics of the events received from the civicbodies Urgency to Probability Impact of resolve of event City Event Typethe event the event occurrence Delhi Traffic High High 1 Congestion FireMedium Very High 0.3 Venice Flooding High High 1 Traffic Low Low 0.15congestion

Post determining the type of the event (at step 606) of each event, thesecond processor 502 compares the type of the event with the statistics(as depicted in the table 2). Based on the statistics, the secondprocessor 502 determines the impact of the event, urgency of the event,and the probability of the occurrence of the event. Thereafter, in anembodiment, the second processor 502 may utilize the following equationto determine a priority value of the event:

EPV=w1*(Prob)+w2*Ω+w3*UR  (1)

where,

EPV: Priority Value

Prob: Probability of the occurrence of the event;

Ω: Impact of the event;

UR: Measure of urgency; and

{w1, w2, w3}: Weights.

In an embodiment, the second processor 502 may normalize the values of“Prob”, Ω and UR to be in the range of 0 and 1. The weights (such as w1,w2, and w3) may be tunable parameters that can be defined by anyconsumer such as a city agency, transportation agency, etc. In anembodiment, the weights assigned to the one or more attributes may varybased on one or more pre-defined policies of these consumers. Postdetermining the priority value of the events, the second processor 502sorts the events based on the priority values associated with eachevent.

A person skilled in the art would appreciate that the scope of thedisclosure should not be limited to determining the priority value ofthe event, as described above. Various other techniques may be used toprioritize the event without departing from the scope of the disclosure.

At step 610, a notification is transmitted to the one or more concernedauthorities. In an embodiment, the second processor 502 may transmit thenotification through the second transceiver 506. As discussed, the oneor more events received by the application server 106 are categorized inthe one or more categories. In an embodiment, the events in eachcategory may be resolved by a concerned authority. The following tableillustrates an example of types of events and respective concernedauthorities:

TABLE 3 Type of events and respective concerned authorities Types ofevents Concerned Authority Water logging Water department Fire Firedepartment Traffic congestion Traffic police Pot holes Road department

For the events categorized under the event tag/category of waterlogging, the second processor 502 may send the notification to the waterdepartment. In an embodiment, the notification is transmitted to theconcerned authority in order of the priority determined in step 608.

In an embodiment, the second processor 502 may transmit the notificationas a task for the one or more authorities. In an embodiment, the taskmay include a service level agreement according to which the one or moreauthorities may have to operate. Further, the SLA may vary based on thepriority of the event. For example, as a fire event is high on priority,the second processor 502 may transmit the notification to the firedepartment as the task with an associated SLA, e.g., an estimated timeof completion of the task. For instance, the fire department may beexpected to release a fire brigade to the location mentioned in thetask, within 5 minutes of the receipt of the task. As discussed, thetask may include information pertaining to the location of the eventalong with the content capturing the event. The fire department mayanalyze the content to determine the severity of the event andaccordingly send manpower/infrastructure to the location.

Further, in an embodiment, the second processor 502 may transmit thenotification to one or more news agencies. In an embodiment, the one ormore news agencies may utilize the notification as news. Further, thenews agencies may further access the database server 108 to determineone or more trends related to the events and accordingly create newsfrom these trends.

At step 612, the one or more users associated with the user computingdevice (e.g., 102 a) are remunerated. In an embodiment, the secondprocessor 502 sends a second notification to the users indicative of therespective remuneration.

At step 614, a statistical model is created based on the historicaldata. In an embodiment, the second processor 502 creates the statisticalmodel. Prior to creation of the statistical model, the second processor502 extracts the historical data pertaining to the one or more eventsfrom the database server 108. In an embodiment, the historical data issegregated based on the types of events and location of the event. In anembodiment, the statistical model is trained based on the historicaldata.

In an embodiment, the statistical model may have the capability topredict an occurrence of the event. In an embodiment, such predictionmay be transmitted by the second processor 502 to the concernedauthority. The concerned authority may accordingly make arrangements.The statistical model may predict the occurrence of the event temporallyor spatially. For example, the statistical model may predict that inAustralia there is a high possibility of forest fires during the summerseason. Considering another example, the statistical model may be usedto predict traffic congestion on the roads. Various trip planningsystems may utilize this information to determine/plan routes from asource location to a destination location.

A person having ordinary skill in the art would understand that scope ofthe disclosure is not limited to predicting the occurrence of the event.In an embodiment, the statistical model may be used to determine trendsof the event occurrence. In an embodiment, the trends may be locationcentric trends or temporal trends pertaining to the location. Forexample, trends may indicate a number of potholes that have come up onan expressway between a location A and a location B in the last monsoonseason.

In an embodiment, the application server 106 may further generate areport of the one or more events. In an embodiment, the applicationserver 106 may receive a request from a civic authority for a report.The request may include information pertaining to the type of event andlocation. Thereafter, in an embodiment, the application server 106 mayquery the database server 108 for information pertaining to theoccurrence of the event in the location, where the location and theevent type has been specified by the authority in the request. Based onthe information, the application server 106 may generate the report. Thereport may include all the occurrences of event in the location andfeedback related to the event from the one or more users.

Use Case (Public Infrastructure/Service)

For example, the application server 106 receives crowd-sensed datapertaining to a pothole on a major expressway. Thereafter, theapplication server 106 analyzes the content in the crowd-sensed data todetermine whether the quality of the content is up to the mark. If thequality of the content is up to the mark, the application server 106 mayvalidate the crowd-sensed data for completeness and data consistency (asdescribed in step 604).

Thereafter, the application server 106 determines the type of the eventbased on the event tag and the image processing of the content in thecrowd-sensed data. Therefore, in this case, the application server 106determines that the type of the content is “pothole”. Accordingly, theapplication server 106 may inform the road authorities that a pothole ispresent on the major expressway. In an embodiment, the applicationserver 106 may transmit the notification as the task for the roadauthority. The task may include expected completion time, say two weeks.Further, the application server 106 may update the historical data inthe database server 108.

In an embodiment, the users, who reported the event, may provide afeedback to the authority post the resolution of the pothole problem. Inan embodiment, the feedback may be provided to the authorities toimprove their service process.

A person having ordinary skill in the art would understand that thescope of the disclosure is not limited to reporting a pothole event. Inan embodiment, the application server 106 may receive the crowd-senseddata pertaining to reporting of an illegal incidence or an issue in aservice being provided by a government firms such as telephone service,and the like.

Use Case (Private Infrastructure/Service)

A person having ordinary skill in the art would understand that scope ofthe disclosure is not limited to reporting an event indicative ofaberration in public infrastructure/service. In an embodiment, theapplication server 106 may receive the event pertaining to aberration inprivate infrastructure.

For example, the application server 106 receives crowd-sensed datapertaining to a plumbing or an electrical problem in a user's house orlocality. Thereafter, the application server 106 analyzes the content inthe crowd-sensed data to determine whether the quality of the content isup to the mark. If the quality of the content is up to the mark, theapplication server 106 may validate the crowd-sensed data forcompleteness and data consistency (as described in step 604).

Thereafter, the application server 106 determines the type of the eventbased on the event tag and the image processing of the content in thecrowd-sensed data. Therefore, in this case, the application server 106determines that the type of the content is a plumbing problem or anelectrical problem in the user's house/locality, as the case may be.Accordingly, the application server 106 may inform the appropriateauthorities about the determined problem. For instance, in case of aplumbing problem, the application server 106 may send a notification toa private/government contractor/service provider who provides plumbingservices near the user's house/locality. Similarly, in case of anelectrical problem, the application server 106 may send a notificationto a private/government contractor/service provider who provideselectrical repairing services near the user's house/locality.

A person having ordinary skill in the art would understand that thescope of the disclosure is not limited to reporting a plumbing or anelectrical problem in a user's house or locality. In an embodiment, theapplication server 106 may receive the crowd-sensed data pertaining toreporting of various other types of problems faced by a resident (i.e.,the user) of a locality/housing society.

Use Case (Natural Disaster or Environmental Problem/Issue)

For example, the application server 106 receives crowd-sensed datapertaining to a natural disaster or an environmental problem/issue.Thereafter, the application server 106 analyzes the content in thecrowd-sensed data to determine whether the quality of the content is upto the mark. If the quality of the content is up to the mark, theapplication server 106 may validate the crowd-sensed data forcompleteness and data consistency (as described in step 604).

Thereafter, the application server 106 determines the type of the eventbased on the event tag and the image processing of the content in thecrowd-sensed data. Therefore, in this case, the application server 106determines that the type of the content is a natural disaster or anenvironmental problem/issue, as the case may be. Accordingly, theapplication server 106 may inform the appropriate authorities about thedetermined problem. For instance, in case of a natural disaster (e.g.,flood, landslide, etc.), the application server 106 may send anotification to a weather monitoring authority or a disaster managementauthority. Similarly, in case of an environmental problem/issue (e.g.,accumulation of garbage, sanitation issues, improper disposal of harmfulchemicals/wastes, etc.), the application server 106 may send anotification to a municipal authority or a city planning agency.

Disclosed embodiments encompass numerous advantages. Various advantagesof the disclosure include providing an end-to-end city managementsolution with relevant inputs from the residents themselves. As theresidents of a place are well versed with the unique problems associatedwith their place of residence, the inputs provided by such residents arelikely to be very relevant in solving the concerned problem. Further, asthe resident is authenticated before he/she can use the event reportingapplication on his/her computing device, the events reported by suchauthenticated residents is likely to be legitimate. To further validatethe incoming event reports from the residents, the metadata within eachevent report is checked for data quality, data completeness, and dataconsistency.

Further, the metadata within the crowd-sensed data is analyzed todetermine the event type of the event. As described earlier, the eventsmay be categorized/prioritized based on an impact, an urgency, or anoccurrence probability, associated with each event. Thereafter, based onthe category/priority of the event, one or more appropriate authoritiesare notified for the timely resolution of the problem/issue at hand.Further, as already described, the status of the problem/issue istracked based on feedback received from the residents after theappropriate authorities have been notified of the problem/issue.

Another advantage of the disclosure lies in the creation of astatistical model for different types of events based on a historicaldata associated with the events of various types. One or more trendsrelated to the future occurrence of the event in a particular locationmay be determined based on the statistical model. As discussed, theappropriate authorities may be provided reports related to such trends.The authorities may use such trends to cater for future exigencies.

The disclosed methods and systems, as illustrated in the ongoingdescription or any of its components, may be embodied in the form of acomputer system. Typical examples of a computer system include ageneral-purpose computer, a programmed microprocessor, amicro-controller, a peripheral integrated circuit element, and otherdevices, or arrangements of devices that are capable of implementing thesteps that constitute the method of the disclosure.

The computer system comprises a computer, an input device, a displayunit, and the internet. The computer further comprises a microprocessor.The microprocessor is connected to a communication bus. The computeralso includes a memory. The memory may be RAM or ROM. The computersystem further comprises a storage device, which may be a HDD or aremovable storage drive such as a floppy-disk drive, an optical-diskdrive, and the like. The storage device may also be a means for loadingcomputer programs or other instructions onto the computer system. Thecomputer system also includes a communication unit. The communicationunit allows the computer to connect to other databases and the internetthrough an input/output (I/O) interface, allowing the transfer as wellas reception of data from other sources. The communication unit mayinclude a modem, an Ethernet card, or other similar devices that enablethe computer system to connect to databases and networks, such as, LAN,MAN, WAN, and the internet. The computer system facilitates input from auser through input devices accessible to the system through the I/Ointerface.

To process input data, the computer system executes a set ofinstructions stored in one or more storage elements. The storageelements may also hold data or other information, as desired. Thestorage element may be in the form of an information source or aphysical memory element present in the processing machine.

The programmable or computer-readable instructions may include variouscommands that instruct the processing machine to perform specific tasks,such as steps that constitute the method of the disclosure. The systemsand methods described can also be implemented using only softwareprogramming or only hardware, or using a varying combination of the twotechniques. The disclosure is independent of the programming languageand the operating system used in the computers. The instructions for thedisclosure can be written in all programming languages, including, butnot limited to, ‘C’, ‘C++’, ‘Visual C++’ and ‘Visual Basic’. Further,software may be in the form of a collection of separate programs, aprogram module containing a larger program, or a portion of a programmodule, as discussed in the ongoing description. The software may alsoinclude modular programming in the form of object-oriented programming.The processing of input data by the processing machine may be inresponse to user commands, the results of previous processing, or from arequest made by another processing machine. The disclosure can also beimplemented in various operating systems and platforms, including, butnot limited to, ‘Unix’, DOS′, ‘Android’, ‘Symbian’, and ‘Linux’.

The programmable instructions can be stored and transmitted on acomputer-readable medium. The disclosure can also be embodied in acomputer program product comprising a computer-readable medium, or withany product capable of implementing the above methods and systems, orthe numerous possible variations thereof.

Various embodiments of the methods and systems for processingcrowd-sensed data have been disclosed. However, it should be apparent tothose skilled in the art that modifications in addition to thosedescribed are possible without departing from the inventive conceptsherein. The embodiments, therefore, are not restrictive, except in thespirit of the disclosure. Moreover, in interpreting the disclosure, allterms should be understood in the broadest possible manner consistentwith the context. In particular, the terms “comprises” and “comprising”should be interpreted as referring to elements, components, or steps, ina non-exclusive manner, indicating that the referenced elements,components, or steps may be present, or used, or combined with otherelements, components, or steps that are not expressly referenced.

A person with ordinary skills in the art will appreciate that thesystems, modules, and sub-modules have been illustrated and explained toserve as examples and should not be considered limiting in any manner.It will be further appreciated that the variants of the above disclosedsystem elements, modules, and other features and functions, oralternatives thereof, may be combined to create other different systemsor applications.

Those skilled in the art will appreciate that any of the aforementionedsteps and/or system modules may be suitably replaced, reordered, orremoved, and additional steps and/or system modules may be inserted,depending on the needs of a particular application. In addition, thesystems of the aforementioned embodiments may be implemented using awide variety of suitable processes and system modules, and are notlimited to any particular computer hardware, software, middleware,firmware, microcode, and the like.

The claims can encompass embodiments for hardware and software, or acombination thereof.

It will be appreciated that variants of the above disclosed, and otherfeatures and functions or alternatives thereof, may be combined intomany other different systems or applications. Presently unforeseen orunanticipated alternatives, modifications, variations, or improvementstherein may be subsequently made by those skilled in the art, which arealso intended to be encompassed by the following claims.

What is claimed is:
 1. A method for processing crowd-sensed data, the method comprising: receiving, by one or more processors, the crowd-sensed data from a mobile device associated with a user, wherein the crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure; prioritizing, by the one or more processors, the event based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event; and transmitting, by the one or more processors, a notification of the event to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.
 2. The method of claim 1, wherein the metadata comprises at least one of content, a location of the mobile device, a time of sending the metadata, or an event tag, associated with the event.
 3. The method of claim 2 further comprising validating, by the one or more processors, the metadata, wherein the validation comprises at least determining a quality metric of the content in the metadata.
 4. The method of claim 3 further comprising comparing, by the one or more processors, the quality metric with a predetermined threshold, wherein the metadata is validated based on the comparison.
 5. The method of claim 3, wherein the validation further comprises determining a completeness of the metadata.
 6. The method of claim 2 further comprising determining, by the one or more processors, a type of the event associated with the metadata based on the content and the event tag.
 7. The method of claim 6 further comprising categorizing, by the one or more processors, the event in one or more categories based on the type of the event.
 8. The method of claim 7 further comprising defining, by the one or more processors, a new category in the one or more categories when the type of the event is unknown, wherein the event is categorized in the new category.
 9. The method of claim 1 further comprising storing, by the one or more processors, the metadata in a database as a historical data.
 10. The method of claim 9 further comprising generating, by the one or more processors, one or more statistical models based on the historical data, wherein the one or more statistical models predict an occurrence of the event.
 11. The method of claim 1 further comprising remunerating, by the one or more processors, the user of the mobile device for reporting the event.
 12. The method of claim 11 further comprising registering, by the one or more processors, the user, wherein the registration of the user comprises creation of a user profile.
 13. The method of claim 1, wherein the event further corresponds at least a natural disaster.
 14. A system for processing crowd-sensed data, the system comprising: one or more processors configured to: receive the crowd-sensed data from a mobile device associated with a user, wherein the crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure; prioritize the event based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event; and transmit a notification of the event to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata.
 15. The system of claim 14, wherein the metadata comprises at least one of content, a position of the mobile device, a time of sending the metadata from the mobile device, or an event tag, associated with the event.
 16. The system of claim 15, wherein the one or more processors are further configured to validate the metadata, wherein the validation comprises at least determining a quality metric of the content in the metadata.
 17. The system of claim 16, wherein the one or more processors are further configured to compare the quality metric with a predetermined threshold, wherein the metadata is validated based on the comparison.
 18. The system of claim 15, wherein the one or more processors are further configured to determine a type of the event associated with the metadata based on the content and the event tag.
 19. The system of claim 18, wherein the one or more processors are further configured to categorize the event in one or more categories based on the type of the event.
 20. The system of claim 19, wherein the one or more processors are further configured to define a new category in the one or more categories when the type of the event is unknown, wherein the event is categorized in the new category.
 21. The system of claim 14, wherein the one or more processors are further configured to store the metadata in a database as a historical data.
 22. The system of claim 21, wherein the one or more processors are further configured to generate one or more statistical models based on the historical data, wherein the one or more statistical models predict an occurrence of the event.
 23. The system of claim 14, wherein the one or more processors are further configured to remunerate the user of the mobile device for reporting the event.
 24. A computer program product for use with a computer, the computer program product comprising a non-transitory computer readable medium, wherein the non-transitory computer readable medium stores a computer program code for processing crowd-sensed data, wherein the computer program code is executable by one or more processors to: receive the crowd-sensed data from a mobile device associated with a user, wherein the crowd-sensed data corresponds to metadata of an event pertaining to an aberration in at least one of a public service, a public infrastructure, a private service, or a private infrastructure; prioritize the event based at least on a type of the event, a measure of impact of the event, or a measure of urgency to resolve the event; and transmit a notification of the event to an organization responsible to at least resolve the event, based on the prioritizing, wherein the notification comprises at least the metadata. 